Observability Engineering Enters the AI Era
Alan Shimel talks with Liz Fong-Jones, Technical Fellow at Honeycomb, about the evolving role of observability engineering as AI changes how software is built, shipped and validated. Fong-Jones explains that AI is giving teams more ability to create software quickly. That speed also creates a new bottleneck: understanding whether those systems work as intended.

The conversation highlights why observability engineering is becoming more important as AI-assisted development grows. Developers may be able to generate code faster, but teams still need to understand behavior in production. They also need enough context to validate changes, investigate issues and reduce risk when systems become more complex.
Telemetry Is Not the Same as Observability
Fong-Jones explains the difference between telemetry and observability. Telemetry includes logs, metrics and traces that describe what is happening inside a system. Observability is the broader ability to use that data, along with human knowledge and process, to understand the system and make better decisions.

She describes observability as a living property of software, similar to testability or accessibility. A team is never “done” with it. Instead, the goal is to keep improving the system so engineers can move faster with more confidence. In that sense, observability engineering is both technical and organizational.
AI Changes the Observability Loop
The discussion also covers the second edition of Observability Engineering, the O’Reilly book co-authored by Fong-Jones and others from Honeycomb. The new edition reflects how much the field has changed since the first version. Teams no longer need to spend as much time debating whether observability is different from monitoring. They now need to understand how it supports AI-driven software delivery.

Fong-Jones says AI can help reduce toil by generating instrumentation code and helping teams sift through large volumes of telemetry. Agent-assisted workflows can also help engineers ask questions, surface insights and move through investigations faster. That does not remove the need for human judgment. It changes where people spend their time.
Cloud Native Becomes the Default Platform
The episode also explores the relationship between cloud native systems, OpenTelemetry and AI applications. Fong-Jones notes that cloud native has become so successful that many organizations now treat it as the default platform. Containers, Kubernetes and related technologies are now part of the expected substrate for modern software.

For Honeycomb, the business value of observability engineering is helping teams innovate faster while reducing risk. Fong-Jones says Honeycomb uses its own practices internally and then brings those lessons to customers. The result is a focus on helping engineering teams understand production systems, validate AI-driven change and move with more confidence.